计算机工程与应用2024，Vol.60Issue(13)：102-112,11.DOI:10.3778/j.issn.1002-8331.2303-0316
多模态特征自适应融合的虚假新闻检测
Multimodal Feature Adaptive Fusion for Fake News Detection
摘要
Abstract
In order to solve the problem that it is difficult to make full use of graphic and text information in multimodal news detection in social media news and to explore efficient multimodal information interaction methods,an adaptive multimodal feature fusion model for fake news detection is proposed.First,the model extracts and represents news text semantic features,text emotional features,and image-text semantic difference features;then,weighted splicing and fusion of various features are performed by adding adaptive weight parameters to reduce the redundancy introduced by model splicing;finally,the fusion feature is sent to the classifier.Experimental results show that the proposed model outperforms the current state-of-the-art models in evaluation indicators such as F1 score.It effectively improves the performance of fake news detection and provides strong support for the detection of fake news in social media.关键词
虚假新闻检测/情感特征/图像描述/自适应融合Key words
fake news detection/emotional feature/image caption/adaptive fusion分类
信息技术与安全科学引用本文复制引用
王腾,张大伟,王利琴,董永峰..多模态特征自适应融合的虚假新闻检测[J].计算机工程与应用,2024,60(13):102-112,11.基金项目
国家自然科学基金(61806072) （61806072）
河北省高等学校科学技术研究项目(ZD2022082,QN2021213) （ZD2022082,QN2021213）
河北省自然科学基金(F2020202008). （F2020202008）
